Protein Stability Engineering in Staphylococcal Nuclease Using an AI-Neural Network Hybrid System and a Genetic Algorithm
With the recent increases in the use of protein-based therapeutics for the treatment of disease, protein stability engineering is an area of growing importance. Feed forward neural networks are trained to predict mutation induced protein stability changes in Staphylococcal nuclease to an accuracy of 93%. These neural networks are then implemented as part of a larger AI framework that is capable of engineering mutations within a protein sequence to yield the target stability or as the basis for a fitness function of a genetic algorithm capable of producing a population of mutation containing sequences of the desired stability. The AI neural network hybrid and genetic algorithm based approaches are significantly faster than other computational methods of protein engineering and can be utilized for proteins in which structural information is lacking.
KeywordsGenetic Algorithm Protein Stability Network Hybrid Residue Position Target Stability
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